code: def test_disabled_sklearn_autologging_does_not_revert_to_enabled_with_setfit(
      iris_data, setfit_trainer
  ):
      mlflow.autolog()
      mlflow.sklearn.autolog(disable=True)
  
      exp = mlflow.set_experiment(
          experiment_name="setfit_with_sklearn_no_autologging"
      )
  
      # Train and evaluate
      setfit_trainer.train()
      metrics = setfit_trainer.evaluate()
      assert metrics["accuracy"] > 0
  
      # Run inference
      preds = setfit_trainer.model(
          [
              "i loved the new Star Trek show!",
              "That burger was gross; it tasted like it was made from cat food!",
          ]
      )
      assert len(preds) == 2
  
      # Test that autologging does not log since it is manually disabled above.
      with mlflow.start_run(experiment_id=exp.experiment_id) as run:
          model = sklearn.cluster.KMeans()
          X, y = iris_data
          model.fit(X, y)
  
      # Assert that only the run info is logged
      logged_sklearn_data = mlflow.get_run(run.info.run_id)
  
      assert logged_sklearn_data.data.params == {}
      assert logged_sklearn_data.data.metrics == {}
  
      client = mlflow.MlflowClient()
      runs = client.search_runs([exp.experiment_id])
  
      assert (
          len(runs) == 1
      )  # SetFit should not create a run in the experiment
      assert runs[0].info == logged_sklearn_data.info
  
imports: import random
  import numpy
  import optuna
  import pytest
  import sklearn
  import sklearn.cluster
  import torch
  from datasets import load_dataset
  from sentence_transformers.losses import CosineSimilarityLoss
  from setfit import SetFitModel, sample_dataset
  from setfit import Trainer
  from setfit import TrainingArguments
  from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, Trainer, TrainingArguments, pipeline
  import mlflow
label_desc: Use image node_id 12 for calling a global function with example usage: test_disabled_sklearn_autologging_does_not_revert_to_enabled_with_setfit(iris_data, setfit_trainer) without return types